Uncertainty in simulation based multidisciplinary design optimization

The development of an engineering artifact, even the most simple one, is increasingly recognized as a decision-making process based upon available information. Because of the unavoidable presence of uncertainty in the information, engineers are required to cope systematically with uncertainty in order to achieve a competent design. Generally designers have focused on being able to quantify the uncertainty associated with a single discipline performance prediction. This research focuses on a more challenging issue by recognizing that most engineering systems are multidisciplinary in nature and that most system models are developed using a variety of disciplinary design tools/models each having some uncertainty associated with its performance. These multidisciplinary system models are often highly coupled, where the performance predictions of one discipline may be used as inputs by another discipline and vice versa. The resulting uncertainty in the performance predictions for such a system can no longer be answered by the single discipline designer, instead the uncertainty must be estimated by propagating the uncertainty of each discipline through the multidisciplinary system model.; In this research an investigation of how uncertainty propagates through a multidisciplinary system analysis is undertaken. A rigorous derivation for estimating the worst case propagated uncertainty in multidisciplinary systems is developed and validated using Monte Carlo simulation. The methodology accounts for both the uncertainty associated with design inputs and the uncertainty of performance predictions from other disciplinary simulation tools. The methodology has been further extended to provide implicit uncertainty estimates for decomposition-based multidisciplinary design optimization (MDO) frameworks such as Collaborative Optimization (CO), where a consistent system design is not always available. These uncertainty estimates lead to the development of a Robust Collaborative Optimization (RCO) framework which handles propagated uncertainty following the strategy of robust design optimization. The RCO framework enables disciplinary autonomy in system design, while simultaneously accounting for performance uncertainties to ensure robustness of the resulting system.